mlsanityData to 7 Oct 2026

What Do End-Users Really Want? Investigation of Human-Centered XAI for Mobile Health Apps

arXiv 2210.03506 ↗First posted 7 Oct 2022cs.HC cs.AIcited by 0

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Abstract (arXiv)

In healthcare, AI systems support clinicians and patients in diagnosis, treatment, and monitoring, but many systems' poor explainability remains challenging for practical application. Overcoming this barrier is the goal of explainable AI (XAI). However, an explanation can be perceived differently and, thus, not solve the black-box problem for everyone. The domain of Human-Centered AI deals with this problem by adapting AI to users. We present a user-centered persona concept to evaluate XAI and use it to investigate end-users preferences for various explanation styles and contents in a mobile health stress monitoring application. The results of our online survey show that users' demographics and personality, as well as the type of explanation, impact explanation preferences, indicating that these are essential features for XAI design. We subsumed the results in three prototypical user personas: power-, casual-, and privacy-oriented users. Our insights bring an interactive, human-centered XAI closer to practical application.

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References · 16

  1. 2016Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localizationcited by 6,690
  2. 2017A Unified Approach to Interpreting Model Predictionscited by 6,488
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  4. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,543
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  6. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
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  11. 2019One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniquescited by 164
  12. 2017Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciencescited by 84
4 more
  1. 2019Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processescited by 53
  2. 2018The Challenge of Crafting Intelligible Intelligencecited by 46
  3. 2019Human-Centered Artificial Intelligence and Machine Learningcited by 37
  4. 2016Rule Extraction Algorithm for Deep Neural Networks: A Reviewcited by 22

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